DOC: cleanup warnings in tutorials.rst

y-p · y-p · commit 507d477f268e · 2014-01-29T23:23:02.000+02:00
diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst
@@ -24,103 +24,101 @@ that entails.
 Here are links to the v0.1 release. For an up-to-date table of contents, see the `pandas-cookbook GitHub
 repository <http://github.com/jvns/pandas-cookbook>`_.
 
-*  | `A quick tour of the IPython
-     Notebook: <http://nbviewer.ipython.org/github/jvns/pandas-c|%2055ookbook/blob/v0.1/cookbook/A%20quick%20tour%20of%20IPython%20Notebook.ipynb>`_
-     Shows off IPython's awesome tab completion and magic functions.
-*  | `Chapter 1: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%201%20-%20Reading%20from%20a%20CSV.ipynb>`_
-     Reading your data into pandas is pretty much the easiest thing. Even
-     when the encoding is wrong!
-*  | `Chapter 2: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%202%20-%20Selecting%20data%20&%20finding%20the%20most%20common%20complaint%20type.ipynb>`_
-     It's not totally obvious how to select data from a pandas dataframe.
-     Here we explain the basics (how to take slices and get columns)
-*  | `Chapter 3: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%203%20-%20Which%20borough%20has%20the%20most%20noise%20complaints%3F%20%28or%2C%20more%20selecting%20data%29.ipynb>`_
-     Here we get into serious slicing and dicing and learn how to filter
-     dataframes in complicated ways, really fast.
-*  | `Chapter 4: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%204%20-%20Find%20out%20on%20which%20weekday%20people%20bike%20the%20most%20with%20groupby%20and%20aggregate.ipynb>`_
-      Groupby/aggregate is seriously my favorite thing about pandas
-     and I use it all the time. You should probably read this.
-*  | `Chapter 5:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%205%20-%20Combining%20dataframes%20and%20scraping%20Canadian%20weather%20data.ipynb>`_
-     Here you get to find out if it's cold in Montreal in the winter
-     (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes.
-*  | `Chapter 6:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%206%20-%20String%20operations%21%20Which%20month%20was%20the%20snowiest%3F.ipynb>`_
-     Strings with pandas are great. It has all these vectorized string
-     operations and they're the best. We will turn a bunch of strings
-     containing "Snow" into vectors of numbers in a trice.
-*  | `Chapter 7: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%207%20-%20Cleaning%20up%20messy%20data.ipynb>`_
-     Cleaning up messy data is never a joy, but with pandas it's easier.
-*  | `Chapter 8:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%208%20-%20How%20to%20deal%20with%20timestamps.ipynb>`_
-     Parsing Unix timestamps is confusing at first but it turns out
-     to be really easy.
-
+-  `A quick tour of the IPython Notebook: <http://nbviewer.ipython.org/github/jvns/pandas-c|%2055ookbook/blob/v0.1/cookbook/A%20quick%20tour%20of%20IPython%20Notebook.ipynb>`_
+   Shows off IPython's awesome tab completion and magic functions.
+-  `Chapter 1: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%201%20-%20Reading%20from%20a%20CSV.ipynb>`_
+   Reading your data into pandas is pretty much the easiest thing. Even
+   when the encoding is wrong!
+-  `Chapter 2: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%202%20-%20Selecting%20data%20&%20finding%20the%20most%20common%20complaint%20type.ipynb>`_
+   It's not totally obvious how to select data from a pandas dataframe.
+   Here we explain the basics (how to take slices and get columns)
+-  `Chapter 3: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%203%20-%20Which%20borough%20has%20the%20most%20noise%20complaints%3F%20%28or%2C%20more%20selecting%20data%29.ipynb>`_
+   Here we get into serious slicing and dicing and learn how to filter
+   dataframes in complicated ways, really fast.
+-  `Chapter 4: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%204%20-%20Find%20out%20on%20which%20weekday%20people%20bike%20the%20most%20with%20groupby%20and%20aggregate.ipynb>`_
+   Groupby/aggregate is seriously my favorite thing about pandas
+   and I use it all the time. You should probably read this.
+-  `Chapter 5:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%205%20-%20Combining%20dataframes%20and%20scraping%20Canadian%20weather%20data.ipynb>`_
+   Here you get to find out if it's cold in Montreal in the winter
+   (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes.
+-  `Chapter 6:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%206%20-%20String%20operations%21%20Which%20month%20was%20the%20snowiest%3F.ipynb>`_
+   Strings with pandas are great. It has all these vectorized string
+   operations and they're the best. We will turn a bunch of strings
+   containing "Snow" into vectors of numbers in a trice.
+-  `Chapter 7: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%207%20-%20Cleaning%20up%20messy%20data.ipynb>`_
+   Cleaning up messy data is never a joy, but with pandas it's easier.
+-  `Chapter 8:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%208%20-%20How%20to%20deal%20with%20timestamps.ipynb>`_
+   Parsing Unix timestamps is confusing at first but it turns out
+   to be really easy.
 
 
 Lessons for New Pandas Users
 ----------------------------
 
 For more resources, please visit the main `repository <https://bitbucket.org/hrojas/learn-pandas>`_.
 
-*  | `01 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/01%20-%20Lesson.ipynb>`_
-  * Importing libraries
-  * Creating data sets
-  * Creating data frames
-  * Reading from CSV
-  * Exporting to CSV
-  * Finding maximums
-  * Plotting data
+- `01 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/01%20-%20Lesson.ipynb>`_
+  - Importing libraries
+  - Creating data sets
+  - Creating data frames
+  - Reading from CSV
+  - Exporting to CSV
+  - Finding maximums
+  - Plotting data
 
-*  | `02 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/02%20-%20Lesson.ipynb>`_
-  * Reading from TXT
-  * Exporting to TXT
-  * Selecting top/bottom records
-  * Descriptive statistics
-  * Grouping/sorting data
+- `02 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/02%20-%20Lesson.ipynb>`_
+  - Reading from TXT
+  - Exporting to TXT
+  - Selecting top/bottom records
+  - Descriptive statistics
+  - Grouping/sorting data
 
-*  | `03 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/03%20-%20Lesson.ipynb>`_
-  * Creating functions
-  * Reading from EXCEL
-  * Exporting to EXCEL
-  * Outliers
-  * Lambda functions
-  * Slice and dice data
+- `03 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/03%20-%20Lesson.ipynb>`_
+  - Creating functions
+  - Reading from EXCEL
+  - Exporting to EXCEL
+  - Outliers
+  - Lambda functions
+  - Slice and dice data
 
-*  | `04 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/04%20-%20Lesson.ipynb>`_
-  * Adding/deleting columns
-  * Index operations
+- `04 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/04%20-%20Lesson.ipynb>`_
+  - Adding/deleting columns
+  - Index operations
 
-*  | `05 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/05%20-%20Lesson.ipynb>`_
-  * Stack/Unstack/Transpose functions
+- `05 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/05%20-%20Lesson.ipynb>`_
+  - Stack/Unstack/Transpose functions
 
-*  | `06 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/06%20-%20Lesson.ipynb>`_
-  * GroupBy function
+- `06 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/06%20-%20Lesson.ipynb>`_
+  - GroupBy function
 
-*  | `07 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/07%20-%20Lesson.ipynb>`_
-  * Ways to calculate outliers
+- `07 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/07%20-%20Lesson.ipynb>`_
+  - Ways to calculate outliers
 
-*  | `08 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/08%20-%20Lesson.ipynb>`_
-  * Read from Microsoft SQL databases
+- `08 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/08%20-%20Lesson.ipynb>`_
+  - Read from Microsoft SQL databases
 
-*  | `09 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/09%20-%20Lesson.ipynb>`_
-  * Export to CSV/EXCEL/TXT
+- `09 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/09%20-%20Lesson.ipynb>`_
+  - Export to CSV/EXCEL/TXT
 
-*  | `10 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/10%20-%20Lesson.ipynb>`_
-  * Converting between different kinds of formats
+- `10 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/10%20-%20Lesson.ipynb>`_
+  - Converting between different kinds of formats
 
-*  | `11 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/11%20-%20Lesson.ipynb>`_
-  * Combining data from various sources
+- `11 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/11%20-%20Lesson.ipynb>`_
+  - Combining data from various sources
 
 
 Excel charts with pandas, vincent and xlsxwriter
 ------------------------------------------------
 
-*  `Using Pandas and XlsxWriter to create Excel charts <http://pandas-xlsxwriter-charts.readthedocs.org/>`_
+-  `Using Pandas and XlsxWriter to create Excel charts <http://pandas-xlsxwriter-charts.readthedocs.org/>`_
 
 Various Tutorials
 -----------------
 
-* `Wes McKinney's (Pandas BDFL) blog <http://blog.wesmckinney.com/>`_
-* `Statistical analysis made easy in Python with SciPy and pandas DataFrames, by Randal Olson <http://www.randalolson.com/2012/08/06/statistical-analysis-made-easy-in-python/>`_
-* `Statistical Data Analysis in Python, tutorial videos, by Christopher Fonnesbeck from SciPy 2013 <http://conference.scipy.org/scipy2013/tutorial_detail.php?id=109>`_
-* `Financial analysis in python, by Thomas Wiecki <http://nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20Pandas%20Basics.ipynb>`_
-* `Intro to pandas data structures, by Greg Reda <http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/>`_
-* `Pandas and Python: Top 10, by Manish Amde <http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/>`_
-* `Pandas Tutorial, by Mikhail Semeniuk <www.bearrelroll.com/2013/05/python-pandas-tutorial>`_
+- `Wes McKinney's (Pandas BDFL) blog <http://blog.wesmckinney.com/>`_
+- `Statistical analysis made easy in Python with SciPy and pandas DataFrames, by Randal Olson <http://www.randalolson.com/2012/08/06/statistical-analysis-made-easy-in-python/>`_
+- `Statistical Data Analysis in Python, tutorial videos, by Christopher Fonnesbeck from SciPy 2013 <http://conference.scipy.org/scipy2013/tutorial_detail.php?id=109>`_
+- `Financial analysis in python, by Thomas Wiecki <http://nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20Pandas%20Basics.ipynb>`_
+- `Intro to pandas data structures, by Greg Reda <http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/>`_
+- `Pandas and Python: Top 10, by Manish Amde <http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/>`_
+- `Pandas Tutorial, by Mikhail Semeniuk <www.bearrelroll.com/2013/05/python-pandas-tutorial>`_
